The present disclosure relates to a video-based method and system for training a vehicle detection system used in a street occupancy estimation of stationary vehicles using samples collected from a surrogate region proximately located to the area of interest. The disclosure finds application in a parking occupancy detection system. However, it is appreciated that the present exemplary embodiments are also amenable to other like applications, such as vehicle detection and traffic management systems.
A video-based parking management system monitors select parking areas to provide real-time vehicle detection and parking occupancy data. One area that is monitored by the system can include on-street parking lanes which extend along a curb on a street. Video cameras are installed nearby for continuously monitoring the parking area. The cameras provide video feed to a system processor, which analyzes the video data for providing a user with any one of vehicle occupancy, parking availability, vehicle orientation, and parking violation information, etc.
A parking occupancy detection system localizes a parked vehicle within a candidate region where a vehicle is known to potentially exist. While the system can detect passenger vehicles with high accuracy, it may partially detect and/or miss larger-sized vehicles, such as commercial vehicles.
A recently proposed method and system improves accuracy by inputting the detected vehicle features to separate classifiers, each trained offline using samples of passenger and special-type vehicles. The classifiers are trained using vehicles detected in the monitored area of interest. However, because the fraction of larger-sized and special-type vehicles is typically very small relative to passenger vehicles for a street parking area of interest, the performance of detecting these vehicles decreases when a small number of samples is used for training a classifier dedicated to detection of larger-sized and special type vehicles.
The offline training of a classifier for a larger and special-type vehicle type can be time consuming due to the scarcity of positive training samples. A typical training stage requires at least several hundred positive (i.e., special type vehicles) and negative (i.e., other-type vehicles or backgrounds) training samples. A sufficient number of positive training samples from special-type parked vehicles can take a long time to collect due to their relative scarcity. In other words, the training is prolonged because of the small sample size of special-type vehicles appearing in the area of interest. Undesired delays may significantly affect large-scale deployments of a technology where training has to be completed before a system goes live. The training is necessary; however, because one missed detection of a special-type vehicle can result in system error or a missed parking violation. A missed detection can furthermore prevent useful information from being provided to users, such as where the special-type vehicle is an emergency vehicle responding to a situation.
Fortunately, the vehicles travelling along a traffic lane near a monitored on-street parking area are typically present at a much higher rates and have similar poses compared to the stationary vehicles parked in the parking area. Accordingly, a method is desired for improving a speed of the offline training stage of classifiers—particularly by increasing the sample size available for the special-type vehicle classifier.